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A Survey of Three-Dimensional Flight Path Planning for Unmanned
Aerial Vehicle
Baoye Song, Gaoru Qi, Lin Xu∗
College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao 266590
E-mail: songbaoye@sdust.edu.cn, 1281034153@qq.com, xulin724@163.com
Abstract: The unmanned aerial vehicles (UAVs) have been widely used in various fields in recent years, where the
three-dimensional (3D) flight path planning is very important to realize the autonomous flight and complete the desired
missions for the UAVs. Although the 3D flight path planning have become an active topic in the filed of the UAVs, there
are still several critical problems to be further investigated to the authors’ knowledge. Motivated by the aforementioned
considerations, the purpose of this survey is to present several representative approaches already proposed in the literature
and provide some problems to be further investigated for the 3D flight path planning of the UAVs.
Key Words: Unmanned Aerial Vehicle, UAV, Three-Dimensional Path Planning, Flight Path Planning
1 Introduction
In recent years, the unmanned aerial vehicles (UAVs) have
been widely used in various fields, such as survey, recon-
naissance, surveillance, rescue and delivery [1–5], which
are increasingly attracting the attentions of scientists and
engineers all over the world. In the various applications of
the UAVs, the three-dimensional (3D) flight path planning,
which is to explore a feasible and optimal flight path be-
tween the start and the destination in the 3D environment,
is of great importance to realize the autonomous flight and
complete the desired missions for the UAVs.
Generally, the 3D flight path planning of the UAVs is a
complex multi-objective optimization problem with several
constraints [6], which can be classified into global/off-line
flight path planning and local/on-line flight path planning,
according to the available environmental information of the
working space [7]. For the off-line flight path planning, the
information of the environment is often completely known
previously, and the generated flight path is usually taken as
an expected trajectory to be followed by the UAVs. If the
UAV is working in a dynamic environment whose infor-
mation is partially or completely unknown, it is necessary
to explore the environment by using the sensors of itself
and plan an optimal flight path in real time. Up to now,
the 3D flight path planning have become an active topic in
the filed of the UAVs, see [8–13] for more details, but to
the authors’ knowledge, there are still several critical prob-
lems to be further investigated, especially for some issues
such as incomplete environmental information, real-time
path planning, kinematics/dynamics constraint and perfor-
mance optimization [14–19], etc.
Motivated by the aforementioned considerations, the pur-
pose of this survey is to present several representative ap-
This work is supported by the National Natural Science Foundation
of China under Grants 61703242 and 61703245.
∗Corresponding author. Email: xulin724@163.com (L. Xu).
proaches already proposed in the literature and provide
some problems to be further investigated for the 3D flight
path planning of UAVs. The remaining part of this paper is
arranged as follows. The modelling of the working space
is presented in Section 2, including the modelling of terrain
and threat. In Section 3, the performance criteria of the 3D
flight path planning is addressed. Section 4 details the path
planning algorithms, including the graph-based algorithms,
the heuristic search algorithms, the field-based algorithms,
the intelligent optimization algorithms as well as their sub-
classes. In Section 5, the path smoothing is discussed for
the smooth flight path planning of UAVs. Finally, the paper
is concluded and some problems to be further investigated
in the future are provided.
2 Modelling of the Working Space
The 3D flight path planning of UAVs is a non-deterministic
polynomial (NP)-hard problem with multiple constraints.
To deal with this problem, the modelling of the working
space, which mainly includes the modelling of terrain and
threat in the working space, is of practical importance to
improve the efficiency and reduce the cost for the flight
path planning of UAVs.
2.1 Modelling of Terrain
In practice, a large amount of UAVs are used for the tasks
of combat, reconnaissance and rescue, which are often car-
ried out in the vast and complex spaces such as mountains,
forests and urban environments. As such, the modelling
of terrain is of great significance to describe the working
space for the 3D flight path planning of UAVs.
With the development of the modern information tech-
nology, the terrain information of the environment can
be acquired more accurately. Compared with the two-
dimensional geographic information, the altitude of the
working space, which is usually represented by the dense
3D coordinates, is vital to supply the height information of
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2019 IEEE
the terrain for the flight path planning of UAVs. For in-
stance, the information of terrain is constructed based on
the DEM (digital elevation model) data released by USGS
(U. S. Geological Survey) in [21] to obtain the accurate
representation of the terrain for the 3D flight path planning
of UAVs. In [22], the two-dimensional grids are employed
to decompose the terrain into similar cells, the elements of
them represent the terrain elevation.
It is noted that the elevation information can also be ob-
tained by using the mathematical fitting methods to form
a complete topographic surface. For example, the cardinal
spline is applied in [23] to make the specified curve cross-
ing over all of the control points, thus the computation load
is reduced by avoiding the repeated interpolations of the
elevation at different positions. In addition, many mathe-
matical functions have been exploited to simulate the to-
pographic information of the terrain such as trigonometric
functions [24–26] and exponential functions [27], and the
smoothing methods with limited slope and curvature are u-
tilized in [28] to achieve the comprehensive smoothness of
the topography, hence the complete terrain is obtained and
the planning efficiency is also further improved.
2.2 Modelling of Threat
During the flight, many threats will be encountered by the
UAVs in the working environment, e.g. big trees, tall build-
ings and power line towers, to just name a few. Besides, the
threats such as radars, missiles and anti-aircraft artilleries
will also be confronted by the UAVs in the military appli-
cations.
In the current literature, the threats of the working space
are usually modelled by some typical shapes, e.g. cube,
cylinder and sphere, whose boundaries are extended ac-
cording to the size of the UAV. Thus, a UAV can be taken
as a point in the flight path planning and it will not collide
with the threats by following the generated feasible flight
path. For example, different shapes are employed to model
the threats according to the slenderness ratio of the obsta-
cles, which are usually amplified to reduce the modelling
error because the exact coordinates are hard to be obtained
for the real geometric centers [8]. All threats are described
as cylinders with a constant moving speed in [24] to sim-
ulate the dynamic working environment. In [29], the om-
nidirectional threats of radars and missiles are regarded as
geometric sphere. In [23], the square prisms with different
heights are utilized to model the urban environment.
Except for the situations mentioned above, some other un-
certain threats are often encountered by the UAVs, e.g. bad
weather and mobile targets, which make the modelling of
threats more difficult. For example, in order to avoid the
bad weather in the flight path planning, each discrete cell
is evaluated and given a risk value to represent the weather
condition [30].
Furthermore, the integrated threat model has been proposed
for the 3D flight path planning of UAVs in [31] with the
consideration of information sharing between the threat
netting, which consists of several active threats with the
exchange of resources and information. Sometimes, the
threats are coupled with each other, i.e., the variation of
one threat will cause the alteration of another, which will
increase the UAV’s probability of being detected and col-
liding with the threats in the combat environment. In [32],
each grid cell is evaluated and a threat network model is es-
tablished using the mutual support of threats. Three differ-
ent variables are defined in [28] to represent the probability
of threat detection in various situations, hence to handle
the problem of information sharing among various threat-
s. In [31], the exchanges of information between the air
defense threat networks are analyzed, and the models of
connectivity and threat cost are established.
3 Performance Criteria of the 3D Flight Path
Planning
The 3D flight path planning of UAVs can be regarded as an
optimization problem, whose general performance criteria
can be formulated as follows:
min f(x)
s.t. pm(x)≤0,m=1,2,···,i,
qn(x)=0,n=1,2,···,j,
(1)
where f(x)is the multi-objective cost function (also called
objective function or fitness function) to evaluate the gen-
erated 3D flight path, whose performance criteria can in-
clude the path distance, flight time and energy consump-
tion, etc; pm(x)and qn(x)are respectively the inequality
and equality constraints, including the constraints such as
terrain, threat and kinematics/dynamics, etc. In the liter-
ature, the factors of the above mentioned multi-objective
cost function are usually allocated with different weight-
s to coordinate the relative importance of each other [33].
However, the weights of the factors are largely depended on
the previous experiences and personal preferences [27,29].
It is worth noting that the constraints of kinematics and dy-
namics will largely affect the results of 3D flight path plan-
ning of the UAVs, whose constraints include but not limited
to [34]:
•Maximum turning angle, which is the maximum angle
that the UAV can turn in the horizontal direction.
•Maximum diving/climbing angle, which is the maxi-
mum angle that the UAV can dive/climb in the vertical
direction.
•Maximum/Minimum altitude, which is the maximum
altitude to save the energy consumption of the UAV
and the minimum altitude to decrease the likelihood
of being detected in the enemy territory.
Actually, the constraints of the performance criteria are of-
ten described by the penalty function, so that the 3D flight
path planning of the UAVs will be finally translated into an
unconstrained optimization problem, which can be solved
by using the heuristic intelligent optimization algorithms
discussed in the following sections.
4 Path Planning Algorithms
By now, a variety of algorithms have been reported for the
3D flight path planning of UAVs, e.g. the graph-based al-
gorithms, the heuristic search algorithms, the field-based
algorithms, and the intelligent optimization algorithms, etc.
5080 The 31th Chinese Control and Decision Conference (2019 CCDC)
4.1 Graph-based Algorithms
The graph-based algorithms mainly include the Visibility
Graph, the Voronoi Diagram, the Rapidly-exploring Ran-
dom Trees (RRT), and the Probabilistic Roadmap (PRM),
which will be discussed in this section.
4.1.1 Visibility Graph
In the Visibility Graph method, the obstacles in the en-
vironment are represented by polygons or polyhedrons,
whose vertices are connected to generate the visible path-
s. In [35], the Visibility Graph is applied for the 3D flight
path planning of the UAVs, where the obstacles are regard-
ed as polygons and the Dijkstra algorithm is used to find
the shortest path. Although this method is easy to be im-
plemented, it can not guarantee the full collision avoidance
with the obstacles in the 3D environment. Moreover, the
computation load will be greatly increased with the increas-
ing of obstacles and environment complexity [36].
4.1.2 Voronoi Diagram
The Voronoi diagram is composed of several polygonal
sides, which are produced by the perpendicular bisectors
of the connected paths between the vertices of the obsta-
cles. In [37], the Voronoi diagram is applied to produce
the initial path of the UAVs. In [38], the distributed threat
centers are acted as the vertices to generate a Voronoi dia-
gram, and then the Dijkstra algorithm is employed to seek
the optimal path based on the established threat indicators.
4.1.3 Rapidly-exploring Random Trees
In the RRT method, a root node is taken as the starting
point, and a tree is randomly expanded by adding leaf n-
odes through random sampling. In [41], an improved R-
RT algorithm is developed to generate the collision-free
waypoints in the complex natural environment, and a path
pruning algorithm is proposed to delete the redundant way-
points. In [42], a geometric method, in which the obsta-
cles are arranged in a tree list, is presented to devise the
collision-avoidance paths for the 2D and 3D path planning
of the UAVs. In [43], a new spline-RRT∗algorithm is put
forward to produce the smooth paths without further path
optimization.
4.1.4 Probabilistic Roadmap
The PRM is to generate several waypoints according to cer-
tain probability distribution in the search space, and the
complete path is produced by connecting the feasible way-
points that are outside of the obstacles. In [39], a random
road map is constructed by using the random sampling
method, i.e., the sampling point is included into the road
map if there is a feasible path between the sampling point
and the node on the path. In [40], a new algorithm based
on 3D graph theory is presented to avoid the collision with
the obstacles, where an adaptive heuristic cost function is
adopted to limit the complexity of the algorithm and the
latest information can be used to replan the path.
It should be mentioned that the space exploring is usual-
ly required for the graph-based algorithms before the path
optimization. So that, the graph should be updated with
the variation of the working environment, which makes it
not suitable for the real-time path planning in some cas-
es. Additionally, the performance constraints of UAVs are
scarcely considered in the graph generating and path opti-
mization.
4.2 Heuristic Search Algorithms
The heuristic search algorithms to handle the issue of path
planning mainly include the A∗algorithm, the D∗algorith-
m and their derivatives.
In [21], the three-dimensional working space is reduced
to be a two-dimensional surface by using the concept of
virtual terrain, and then the improved A∗algorithm is ap-
plied to generate the path trajectory. In [44], an improved
A∗algorithm with variable step-length is developed for the
path planning of multi-UAVs covert attack. In [45], a DEM
based A∗is combined with the Bresenham line-drawing al-
gorithm to generate a satisfied path that is compatible with
the constraints of the UAV’s dynamics.
In [46], an improved D∗Lite algorithm is used to complete
the path planning of the UAVs in the dynamic 3D environ-
ment. A Focused D algorithm is presented for the real-time
3D path planning of the UAVs in [47], but it can not find
the optimal path in real-time when the obstacle is too large.
In [30], the extensions of Lazy Theta∗algorithm are pro-
posed for the path planning of the UAVs considering the
flight of multiple drones and dynamic planning in the bad
weather.
4.3 Field-based Algorithms
The artificial potential field is a typical field-based algorith-
m that is suitable for the online path planning. An improved
artificial potential filed algorithm is put forward in [26] to
avoid the UAV falling into the shock area and local pole by
setting a guiding point in the dynamic environment. The
concept of rolling window is introduced in [48] to detect
the static and dynamic obstacles, and the artificial potential
filed algorithm is combined with the improved rolling plan
to plan the 2D and 3D paths for the UAVs in the complex
dynamic environment. In [49], a new method combining
the virtual field and A∗algorithm is developed for the path
replanning of the UAVs in the 3D environment. In [50],
a method based on the interfered fluid dynamical system is
proposed to fulfill the requirements of the 3D path planning
of UAVs.
4.4 Intelligent Optimization Algorithms
A large amount of intelligent optimization algorithms have
been used for the 3D path planning of UAVs in recen-
t years [7, 14], e.g. the genetic algorithm, the ant colony
algorithm, the particle swarm optimization algorithm, and
the wolf pack algorithm, etc.
In [51], the genetic algorithm is utilized to optimize the tra-
jectory connected by the straight lines in the azimuth space.
An improved multi-agent co-evolutionary algorithm is de-
veloped to deal with the problem of 3D path planning of the
UAVs in [27]. In [52], a multi-objective differential evolu-
The 31th Chinese Control and Decision Conference (2019 CCDC) 5081
tion based path planning algorithm is developed to generate
a set of optimal paths for multi-UAVs in a 3D environment
with obstacles and radar threats. In [33], an improved wolf
pack search algorithm with the operators of crossover and
mutation is proposed to complete the 3D path planning for
the rotor-wing UAV in the real and fake space. In [28],
an improved ant colony algorithm is combined with the ar-
tificial potential field to realize the real-time path planning
under threat networking, where a modified pheromone con-
centration updating method is proposed to improve the de-
fects of the ant colony algorithm, e.g. long searching time
and local trapping. In [53], the fruit fly optimization al-
gorithm is utilized to devise the smooth paths of the UAV
based on the B-spline in the complex environment. In [54],
an improved neural network algorithm is proposed to ob-
tain the optimal path of multi-UAVs in the 3D combat en-
vironment. In [55], an improved bat algorithm combined
with differential evolution is employed to search the op-
timal path of the UAVs in 3D environment. In [34], an
improved particle swarm optimization algorithm is applied
to the 3D path planning of the UAVs to realize the terrain
following and terrain avoidance. In [56], a grey prediction
method is used to predict the threat of moving targets, and
the optimal track points are obtained by using the improved
chaotic fish swarm algorithm. By using the proposed algo-
rithms, not only the static obstacles but also the dynamic
threats are avoided.
It should be mentioned that the intelligent optimization al-
gorithms have been increasingly widely used in the path
planning of the mobile robot in both 2D and 3D cases [57].
But all the intelligent optimization algorithms have to over-
come several frequently encountered harmful phenomena
such as local trapping and early convergence.
5 Path Smoothing
It should be highlighted that the smooth path is a desirable
performance for the 3D path planning of UAVs in the real
flight [58]. On the one hand the constraints of the nonholo-
nomic UAVs require the continuous command of angle to
implement the accurate flight turning, on the other hand the
zero curvature is necessary at certain waypoints to provide
the continuous curvature for the flight of the UAVs. As
such, a great amount of approaches have been proposed for
the smooth 3D path planning of the UAVs in recent years.
For example, the continuous-curvature steering with zero
end curvature is generated by combining the circular arcs,
clothoids and line segments [59]. However, the segments
of clothoid can only be calculated numerically by using the
Fresnel integrals. In [60], the smooth path inside a bound-
ed airspace is produced by using the Bezier curve, whose
smoothness can be enhanced by increasing the order of the
curve. Nevertheless, the additional computation is required
for the high-order polynomials. In addition, the position
variation of one control point will lead to the whole vari-
ation of the Bezier curve. Thus, the B-splines with lower-
order is exploited for the path generation, which unfortu-
nately is subject to discontinuous curvature at the connect-
ing waypoints.
Moreover, in the environment with obstacles, a two-step
approach is usually used for the path-planning [61]. In the
first step, the obstacle-avoidance straight line global path-
s are generated by using the path planners such as RRT
and PRM, and then in the second step, the global paths are
piecewise smoothed by using the smooth curves, e.g. the
cubic Bezier curve [62], the η3-spline [63] and the cubic
polynomial spline [64], etc. It is clear that this methodolo-
gy will lead to suboptimal smooth path for the task of path
planning. Besides, there is few literature on 3D smooth
path planning of the UAVs in the current research.
6 Conclusion
This paper is concerned with a survey of the 3D flight path
planning for the UAVs, where several representative ap-
proaches proposed in the literature are presented, including
the modelling of terrain and threat, the performance crite-
ria, the path planning algorithms, and the path smoothing,
etc.
By reviewing the current literature, it can be concluded that
there are several problems to be further investigated in the
future. For example, some harmful phenomena, such as lo-
cal trapping and early convergence, are frequently encoun-
tered by the intelligent optimization algorithms based path
planning methods; and the constraints of kinematics and
dynamics are still scarcely considered in the problem of s-
mooth 3D path planning of the UAVs.
In the future, we will focus on the following problems but
not limited to: (1) how to develop the new strategy to over-
come the frequently appearing harmful phenomena in the
intelligent optimization algorithm; (2) how to use the new
intelligent algorithms to deal with the problem of 3D path
planning of UAVs; (3) how to link the smoothness criteria
with kinematics/dynamics constraints directly into the op-
timization of the planning path, rather than smoothing the
straight line path afterwards, etc. These problems should be
pay more attention for the practical 3D flight path planning
of UAVs, and the thorough solution of them will greatly
advance the performance of the planning path.
7 Acknowledgement
This work is supported in part by the National Natural
Science Foundation of China under Grants 61703242 and
61703245.
REFERENCES
[1] A. Lavaei, and M. A. A. Atashgah, “Optimal 3D trajectory
generation in delivering missions under urban constraints for
a flying robot,” Intelligent Service Robotics, vol. 10, no. 3,
pp. 241-256, Jul. 2017.
[2] P. Yao, H. Wang, and Z. Su, “Real-time path planning of un-
manned aerial vehicle for target tracking and obstacle avoid-
ance in complex dynamic environment,” Aerospace Science
and Technology, vol. 47, pp. 269-279, Dec. 2015.
[3] J. Wu, H. Wang, N. Li, P. Yao, Y. Huang, and H. Yang,
“Path planning for solar-powered UAV in urban environmen-
t,” Neurocomputing, vol. 275, pp. 2055-2065, Jan. 2018.
[4] C. Yin, Z. Xiao, X. C, X. Xi, P. Yang, and D. Wu, “Offline
and online search: UAV multiobjective path planning under
dynamic urban environment,” IEEE Internet of Things Jour-
nal, vol. 5, no. 2, pp. 546-558, Apr. 2018.
5082 The 31th Chinese Control and Decision Conference (2019 CCDC)
[5] Y. Lin, and S. Saripalli, “Path planning using 3D Dubin-
s curve for unmanned aerial vehicle,” In: Proceedings of
2014 International Conference on Unmanned Aircraft Sys-
tems, Orlando, USA, May 2014, pp. 296-304.
[6] P. Kumar, S. Garg, A. Singh, S. Batra, N. Kumar, and I. You,
“MVO-based 2-D path planning scheme for providing quali-
ty of service in UAV environment,” IEEE Internet of Things
Journal, vol. 5, no. 3, pp. 1698-1707, Jun. 2018.
[7] Y. Zhao, Z. Zheng, and Y. Liu, “Survey on computational-
intelligence-based UAV path planning,” Knowledge-Based
Systems, vol. 158, pp. 54-64, Oct. 2018.
[8] Y. Chen, J. Yu, Y. Mei, Y. Wang, and X. Su, “Modified central
force optimization (MCFO) algorithm for 3D UAV path plan-
ning,” Neurocomputing, vol. 171, pp. 878-888, Jan. 2016.
[9] X. Zhang, and H. Duan, “An improved constrained differen-
tial evolution algorithm for unmanned aerial vehicle global
route planning,” Applied Soft Computing, vol. 26, pp. 270-
284, Jan. 2015.
[10] T. T. Mac, C. Copot, D. T. Tran, and R. D. Keyser, “Heuristic
approaches in robot path planning: A survey,” Robotics and
Autonomous Systems, vol. 86, pp. 13-28, Dec. 2016.
[11] E. Besada-Portas, L. de la Torre, J. M. de la Cruz, and B. de
Andres-Toro, “Evolutionary trajectory planner for multiple
UAVs in realistic scenarios,” IEEE Transactions on Robotics,
vol. 26, no. 4, pp. 619-634, Aug. 2010.
[12] H. Duan, and L. Huang, “Imperialist competitive algo-
rithm optimized artificial neural networks for UCAV glob-
al path planning,” Neurocomputing, vol. 125, pp. 166-171,
Feb. 2014.
[13] Y. Chen, J. Yu, Y. Mei, S. Zhang, X. Ai, and Z. Jia, “Tra-
jectory optimization of multiple quad-rotor UAVs in collab-
orative assembling task,” Chinese Journal of Aeronautics,
vol. 29, no. 1, pp. 184-201, Feb. 2016.
[14] W. P. Coutinho, M. Battarra, and J. Fliege, “The unmanned
aerial vehicle routing and trajectory optimization problem, a
taxonomic review,” Computers and Industrial Engineering,
vol. 120, pp. 116-128, Jun. 2018.
[15] L. De Pilippis, G. Giorgio, and F. Quagliotti, “Path planning
strategies for UAVs in 3D environments,” Journal of Intel-
ligent and Robotic Systems, vol. 65, no. 1-4, pp. 247-264,
Jan. 2012.
[16] A. Chakrabarty, and J. W. Langelaan, “Energy-based long-
range path planning for soaring-capable unmanned aerial ve-
hicles,” Journal of Guidance Control and Dynamics, vol. 34,
no. 4, pp. 1002-1015, Aug. 2011.
[17] L. Babel, “Three-dimensional route planning for unmanned
aerial vehicles in a risk environment,” Journal of Intelligent
and Robotic Systems, vol. 71, no. 2, pp. 255-269, Aug. 2013.
[18] K. Wu, T. Xi, and H. Wang, “Real-time three-dimensional
smooth path planning for unmanned aerial vehicles in com-
pletely unknown cluttered environments,” In: Proceedings of
the 2017 IEEE Region 10 Conference, Malaysia, Nov. 2017,
pp. 2017-2022.
[19] X. Zhang, X. Lu, S. Jia, and X. Li, “A novel phase angle-
encoded fruit fly optimization algorithm with mutation adap-
tation mechanism applied to UAV path planning,” Applied
Soft Computing, vol. 70, pp. 371-388, Sep. 2018.
[20] B. Wehbe, S. Bazzi, and E. Shammas, “Novel three-
dimensional optimal path planning method for vehicles with
constrained pitch and yaw,” Robotica, vol. 35, pp. 2157-
2176, Nov. 2017.
[21] Z. Qi, Z. Shao, Y. S. Ping, L. M. Hiot, and Y. K. Leong, “An
improved heuristic algorithm for UAV path planning in 3D
environment,” In: Proceedings of 2010 Second International
Conference on Intelligent Human-Machine Systems and Cy-
bernetics, Nanjing, China, Aug. 2010, pp. 258-261.
[22] V. Roberge, M. Tarbouchi, and G. Labonte, “Comparison of
parallel genetic algorithm and particle swarm optimization
for real-time UAV path planning,” IEEE Transactions on In-
dustrial Informatics, vol. 9, no. 1, pp. 132-141, Feb. 2013.
[23] Y. Liu, X. Zhang, X. Guan, and D. Delahaye, “Adaptive
sensitivity decision based path planning algorithm for un-
manned aerial vehicle with improved particle swarm opti-
mization,” Aerospace Science and Technology, vol. 58, p-
p. 92-102, Nov. 2016.
[24] B. Zhang, and H. Duan, “Three-dimensional path planning
for uninhabited combat aerial vehicle based on predator-
prey pigeon-inspired optimization in dynamic environmen-
t,” IEEE/ACM Transactions on Computational Biology and
Bioinformatics, vol. 14, pp. 97-107, Jan./Feb. 2017.
[25] Y. V. Pehlivanoglu, “A new vibrational genetic algorithm
enhanced with a Voronoi diagram for path planning of au-
tonomous UAV,” Aerospace Science and Technology, vol. 16,
no. 1, pp. 47-55, Jan./Feb. 2012.
[26] X. Chen, and J. Zhang, “The three-dimension path planning
of UAV based on improved artificial potential field in dynam-
ic environment,” In: Proceedings of 2013 5th International
Conference on Intelligent Human-Machine Systems and Cy-
bernetics, Hangzhou, China, Aug. 2013, pp. 144-147.
[27] Z. Peng, L. Sun, J. Chen, and J. Wu, “Path planning of multi-
ple UAVs low-altitude penetration based on improved multi-
agent coevolutionary algorithm,” In: Proceedings of the 30th
Chinese Control Conference, Yantai, China, Jul. 2011, p-
p. 4056-4061.
[28] X. Ling, “Effective 3-D path planning for UAV in presence
of threat netting,” In: Proceedings of 2015 5th International
Conference on Communication Systems and Network Tech-
nologies, Gwalior, India, Sep. 2015, pp. 1298-1302.
[29] C. Huang, and J. Fei, “UAV path planning based on parti-
cle swarm optimization with global best path competition,”
International Journal of Pattern Recognition and Artificial
Intelligence, vol. 32, no. 6, pp. 1859008, Jun. 2018.
[30] M. Garcia, A. Viguria, and A. Ollero, “Dynamic graph-
search algorithm for global path planning in presence of haz-
ardous weather,” Journal of Intelligent and Robotic Systems,
vol. 69, no. 1-4, pp. 285-295, Jan. 2013.
[31] X. Fu, J. Li, and X. Gao, “Modeling and analysing of air-
defense threat netting,” Acta Armamentarii, vol. 34, no. 7,
pp. 904-909, Jul. 2013.
[32] R. J. Szczerba, “Threat netting for real-time, intelligen-
t route planners,” In: Proceedings of Information, Decision
and Control, Adelaide, Australia, Feb. 1999, pp. 377-382.
[33] Y. Chen, Y. Mei, J. Yu, X. Su, and N. Xu, “Three-
dimensional unmanned aerial vehicle path planning using
modified wolf pack search algorithm,” Neurocomputing,
vol. 266, pp. 445-457, Nov. 2017.
[34] Q. Wang, A. Zhang, and L. Qi, “Three-dimensional path
planning for UAV based on improved PSO algorithm,” In:
Proceedings of the 26th Chinese Control and Decision Con-
ference, Changsha, China, Jun. 2014, pp. 3981-3985.
[35] Y. Kuwata, and J. How, “Three dimensional receding hori-
zon control for UAVs,” In: Collection of Technical Papers -
AIAA Guidance, Navigation, and Control Conference, Prov-
idence, USA, Aug. 2004, pp. 2100-2113.
[36] J. Kim, M. Kim, and D. Kim, “Variants of the quantized vis-
ibility graph for efficient path planning,” Advanced Robotics,
vol. 25, no. 18, pp. 2341-2360, 2011.
[37] T. Han, W. Wu, C. Huang, and Y. Xuan, “Path planning of
The 31th Chinese Control and Decision Conference (2019 CCDC) 5083
UAV based on Voronoi diagram and DPSO,” In: Proceedings
of 2012 International Workshop on Information and Elec-
tronics Engineering, Harbin, China, Mar. 2012, pp. 4198-
4203.
[38] X. Chen, and G. Xu, “The path planning algorithm studying
about UAV attacks multiple moving targets based on Voronoi
diagram,” International Journal of Control and Automation,
vol. 9, no. 1, pp. 281-292, Jan. 2016.
[39] P. O. Pettersson, and P. Doherty, “Probabilistic roadmap
based path planning for an autonomous unmanned heli-
copter,” Journal of Intelligent and Fuzzy Systems: Applica-
tions in Engineering and Technology, vol. 17, no. 4, pp. 395-
405, 2006.
[40] S. Razzaq, C. Xydeas, M. E. Everett, A. Mahmood, and
T. Alquthami, “Three-dimensional UAV routing with decon-
fliction,” IEEE Access, vol. 6, pp. 21536-21551, Apr. 2018.
[41] K. Yang, and S. Sukkariceh, “3D smooth path planning for
a UAV in cluttered natural environments,” In: Proceedings
of IEEE/RSJ International Conference on Intelligent Robots
and Systems, Nice, France, Sep. 2008, pp. 794-800.
[42] X. Liang, G. Meng, Y. Xu, and H. Luo, “A geometrical
path planning method for unmanned aerial vehicle in 2D/3D
complex environment,” Intelligent Service Robotics, vol. 11,
no. 3, pp. 301-312, Jul. 2018.
[43] D. Lee, and D. H. Shim, “Spline-RRT∗based optimal path
planning of terrain following flights for fixed-wing UAVs,”
In: Proceedings of 11th International Conference on U-
biquitous Robots and Ambient Intelligence, Kuala Lumpur,
Malaysia, Nov. 2014, pp. 257-261.
[44] J. Zhao, and J. Zhao, “Path planning of multi-UAVs conceal-
ment attack based on new A∗method,” In: Proceedings of
6th International Conference on Intelligent Human-Machine
Systems and Cybernetics, Hangzhou, China, Aug. 2014, p-
p. 401-404.
[45] H. Liang, H. Bai, R. Sun, and C. Li, “Three-dimensional
path planning based on DEM,” In: Proceedings of the 36th
Chinese Control Conference, Dalian, China, Jul. 2017, p-
p. 5980-5987.
[46] X. Chen, and D. Liu, “An improved D∗Lite algorithm based
3D path planning for UAVs when target is moving,” Electron-
ics Optics and Control, vol. 20, no. 7, pp. 1-5, Jul. 2013.
[47] Y. Chen, Y. Zhao, and H. Wang, “Real time path planning
for UAV based on focused D,” In: Proceedings of the Fourth
International Workshop on Advanced Computational Intelli-
gence, Wuhan, China, Oct. 2011, pp. 80-85.
[48] Q, Xue, P. Cheng, N. Cheng, and X. Zou, “Dynamic ob-
stacle avoidance path planning of UAVs,” In: Proceedings
of the 34th Chinese Control Conference, Hangzhou, China,
Jul. 2015, pp. 8860-8865.
[49] Z. Dong, Z. Chen, R. Zhou, and R. Zhang, “A hybrid ap-
proach of virtual force and A∗search algorithm for UAV
path re-planning,” In: Proceedings of 6th IEEE Conference
on Industrial Electronics and Applications, Beijing, China,
Jun. 2011, pp. 1140-1145.
[50] H. Wang, W. Lyu, P. Yao, X. Liang, and C. Liu, “Three-
dimensional path planning for unmanned aerial vehicle based
on interfered fluid dynamical system,” Chinese Journal of
Aeronautics, vol. 28, no. 1, pp. 229-239, Jan. 2015.
[51] J. Peng, X. Sun, F. Zhu, and J. Zhang, “3D path planning
with multi-constrains based on genetic algorithm,” In: Pro-
ceedings of 2008 the 27th Chinese Control Conference, Kun-
ming, China, Jul. 2008, pp. 94-97.
[52] M. Zhao, L. Zhao, X. Su, P. Ma, and Y. Zhang, “A coop-
erative path planning and smoothing algorithm for UAVs in
three dimensional environment,” In: Proceedings of Fourth
International Conference on Instrumentation and Measure-
ment, Computer, Communication and Control, Harbin, Chi-
na, Sep. 2014, pp. 274-278.
[53] X. Zhang, S. Jia, X. Li, and M. Jian, “Design of the fruit fly
optimization algorithm based path planner for UAV in 3D en-
vironments,” In: IEEE International Conference on Mecha-
tronics and Automation, Takamatsu, Japan, Aug. 2017, p-
p. 381-386.
[54] X. Chen, and Y. Ai, “Multi-UAV path planning based on im-
proved neural network,” In: Proceedings of the 30th Chinese
Control and Decision Conference, Shenyang, China, June,
2018, pp. 354-359.
[55] G. Wang, H. E. Chu, and S. Mirjalili, “Three-dimensional
path planning for UCAV using an improved bat Algorith-
m,” Aerospace Science and Technology, vol. 49, pp. 231-238,
Feb. 2016.
[56] H. Shen, J. Chen, H. Li, and Z. Zhou, “Research on real-time
flight path planning of UAV based on grey prediction,” In:
Proceedings of 9th International Symposium on Computa-
tional Intelligence and Design, Hangzhou, China, Dec. 2016,
pp. 62-67.
[57] B. Song, Z. Wang, and L. Sheng, “A new genetic algorithm
approach to smooth path planning for mobile robots,” Assem-
bly Automation, vol. 36, no. 2, pp. 138-145, Apr. 2016.
[58] S. Upadhyay, and A. Ratnoo, “Smooth path planning for un-
manned aerial vehicles with airspace restrictions,” Journal of
Guidance, Control, and Dynamics, vol. 40, no. 7, pp. 1596-
1612, Jul. 2017.
[59] T. Fraichard, and A. Scheuer, “From Reeds and Shepps to
continuous-curvature paths,” IEEE Transactions on Robotic-
s, vol. 20, no. 6, pp. 1025C1035, Dec. 2004.
[60] M. Lizarraga, and G. Elkaim, “Spatially deconflicted path
generation for multiple UAVs in a bounded airspace,” In:
Proceedings of the IEEE/ION Position, Location and Navi-
gation Symposium, Piscataway, USA, May 2008, pp. 1213-
1218.
[61] D. Jung, and P. Tsiotras, “On-line path generation for un-
manned aerial vehicles using B-spline path templates,” Jour-
nal of Guidance, Control, and Dynamics, vol. 36, no. 6, p-
p. 1642-1653, Nov. 2013.
[62] B. Song, Z. Wang, and L. Zou, “On global smooth path plan-
ning for mobile robots using a novel multimodal delayed P-
SO algorithm,” Cognitive Computation, vol. 9, no. 1, pp. 5-
17, Jan. 2017.
[63] B. Song, Z. Wang, L. Zou, L. Xu, and F. E. Alsaadi, “A
new approach to smooth path planning of mobile robots with
kinematic constraints,” International Journal of Machine
Learning and Cybernetics, vol. 10, pp. 107-119, Jan. 2019.
[64] L. Xu, D. Wang, B. Song, and M. Cao, “Global smooth
path planning for mobile robots based on continuous Bezi-
er curve,” In: Proceedings of 2017 Chinese Automation
Congress, Jinan, China, Oct. 2017, pp. 2081-2085.
5084 The 31th Chinese Control and Decision Conference (2019 CCDC)
